FD-CABOSFV区间变量高维数据聚类  

FD-CABOSFV High Dimensional Data Clustering for Interval-scaled Variables

作　　者：武森[1] 张文丽[1] 黄慧敏[1] 叶俞飞[1] 

机构地区：[1]北京科技大学经济管理学院北京100083

出　　处：《信息系统学报》2012年第1期77-87,共11页China Journal of Information Systems

基　　金：国家自然科学基金（70771007）;中央高校基本科研业务费专项资金（FRF-TP-10-006B）.

摘　　要：针对属性取值为区间变量的高维数据聚类问题，提出基于模糊离散化的CABOSFV改进算法——FD-CABOSFV。针对属性组合利用模糊C均值聚类的思想进行属性取值的离散化，并通过A水平截取的方式确定各对象对离散化属性的归属，将其转换为二态变量后利用CABOSFV算法进行聚类。采用三组UCI基准数据集将FD-CABOSFV与著名的K-means聚类算法进行比较，实验结果表明FD-CABOSFV更有效。Abstract FD-CABOSFV, an improved algorithm of CABOSFV based on fuzzy diseretizaton, is proposed for high-dimensional data clustering of interval-scaled variables. It discretizes the data of each attribute portfolio by using the idea of fuzzy C means clustering,and determines each object＇s discretized attribute category by λ cut turning the attributevalue into binary variables,and then uses CABOSFV algorithm to complete clustering. Three UCI benchmark data sets were used to compare FD-CABOSFV with famous K-means clustering algorithm. The empirical tests show that FD-CABOSFV is more effective.

关 键 词：CABOSFV算法 属性组合 模糊离散化 区间变量 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]